A cost-effective manufacturing process recognition approach based on deep transfer learning for CPS enabled shop-floor |
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| Affiliation: | 1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry and Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Xi''an 710072, China;2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore;3. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, School of Construction Machinery, Chang''an University, Xi''an 710064, Shaanxi, China.;1. Department of Production and Systems Engineering, University of Minho, Portugal;2. ALGORITMI Research Centre, University of Minho, Portugal;3. Department of Mechanical Engineering, National Institute of Technology (NIT), Warangal, India;4. Vellore Institute of Technology, Vellore, India;5. Continental Portugal S.A., Portugal;1. Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi''an, Shaanxi 710072, P.R. China;2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, P.R. China |
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| Abstract: | The rapid development of the industrial Internet of Things has promoted manufacturing to develop towards the cyber-physical system, of which highly accurate process recognition plays an important role in achieving proactive monitoring of intelligent manufacturing process. Compared to the traditional handcrafted feature-based method, deep model owns convenience in terms of extracting feature automatically for the recognition. However, training a deep model is time-consuming and also requires large-scale training samples. To solve these problems and obtain high accuracy in the meanwhile, a deep transfer learning-based manufacturing process recognition approach is proposed in this study. A pre-trained model based on a convolutional neural network is used to extract low dimensional features followed by a fine-tuning process to target the specific process recognition task. Experimental verification of two datasets was conducted to demonstrate this cost-effective method. The results showed the proposed method can get better accuracy with less training time and fewer training samples. |
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